Cross-narrative Temporal Ordering of Medical Events

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 998–1008, Baltimore, Maryland, USA, June 23-25 2014. c⃝2014 Association for Computational Linguistics Cross-narrative temporal ordering of medical events Preethi Raghavan∗, Eric Fosler-Lussier∗, No´emie Elhadad† and Albert M. Lai∗ ∗The Ohio State University, Columbus, Ohio †Columbia University, New York, NY {raghavap, fosler}@cse.ohio-state.edu noemie.elhadad@columbia.edu, albert.lai@osumc.edu Abstract Cross-narrative temporal ordering of med- ical events is essential to the task of gen- erating a comprehensive timeline over a patient’s history. We address the prob- lem of aligning multiple medical event se- quences, corresponding to different clin- ical narratives, comparing the following approaches: (1) A novel weighted finite state transducer representation of medi- cal event sequences that enables compo- sition and search for decoding, and (2) Dynamic programming with iterative pair- wise alignment of multiple sequences us- ing global and local alignment algorithms. The cross-narrative coreference and tem- poral relation weights used in both these approaches are learned from a corpus of clinical narratives. We present results us- ing both approaches and observe that the finite state transducer approach performs performs significantly better than the dy- namic programming one by 6.8% for the problem of multiple-sequence alignment. 1 Introduction Discourse structure, logical flow of sentences, and context play a large part in ordering medical events based on temporal relations within a clinical nar- rative. However, cross-narrative temporal rela- tion ordering is a challenging task as it is dif- ficult to learn temporal relations among medical events which are not part of the logically coherent discourse of a single narrative. Resolving cross- narrative temporal relationships between medical events is essential to the task of generating an event timeline from across unstructured clinical narratives such as admission notes, radiology re- ports, history and physical reports and discharge summaries. Such a timeline has multiple applica- tions in clinical trial recruitment (Luo et al., 2011), medical document summarization (Bramsen et al., 2006, Reichert et al., 2010) and clinical decision making (Demner-Fushman et al., 2009). Given multiple temporally ordered medical event sequences generated from each clinical nar- rative in a patient record, how can we combine the events to create a timeline across all the nar- ratives? The tendency to copy-paste text and summarize past information in newly generated clinical narratives leads to multiple mentions of the same medical event across narratives (Cohen et al., 2013). These cross-narrative coreferences act as important anchors for reasoning with in- formation across narratives. We leverage cross- narrative coreference information along with con- fident cross-narrative temporal relation predictions and learn to align and temporally order medical event sequences across longitudinal clinical nar- ratives. We model the problem as a sequence alignment task and propose solving this using two approaches. First, we use weighted finite state machines to represent medical events sequences, thus enabling composition and search to obtain the most probable combined sequence of medical events. As a contrast, we adapt dynamic program- ming algorithms (Needleman et al., 1970, Smith and Waterman, 1981) used to produce global and local alignments for …

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